Emotion identification in FIFA world cup tweets using convolutional neural network

Twitter has gained increasing popularity over the recent years with users generating an enormous amount of data on a variety of topics every day. Many of these posts contain real-time updates and opinions on ongoing sports games. In this paper, we present a convolutional neural network architecture for emotion identification in Twitter messages related to sporting events. The network leverages pre-trained word embeddings obtained by unsupervised learning on large text corpora. Training of the network is performed on automatically annotated tweets with 7 emotions where messages are labeled based on the presence of emotion-related hashtags on which our approach achieves 55.77% accuracy. The model is applied on Twitter messages for emotion identification during sports events on the 2014 FIFA World Cup. We also present the results of our analysis on three games that had significant impact on Twitter users.